A significant challenge with reliability-based design optimization (RBDO) is the high computational cost associated with the double-loop structure that entails a large number of function calls for both the optimization process and reliability analysis. Several decoupling methods have been developed to improve the efficiency of RBDO. In addition, surrogate models have been used to replace the original time-consuming models and improve the computational efficiency. This paper proposes a novel quantile-based sequential RBDO method using Kriging surrogate models for problems with independent constraint functions. An error-controlled adaptive Kriging scheme is integrated to derive accuracy information of surrogate models and develop a strategy that facilitates independent training of the models for the performance function. The proposed independent training avoids unnecessary performance function evaluations while ensuring the accuracy of reliability estimates. Moreover, a new sampling approach is proposed that allows refinement of surrogate models for both deterministic and probabilistic constraints. Five numerical examples are carried out to demonstrate the performance of the proposed method. It is observed that the proposed method is able to converge to the optimum design with significantly fewer function evaluations than the state-of-the-art methods based on surrogate models given the constraint functions are independent.

基于可靠性的设计优化（RBDO）面临的一个重大挑战是与双循环结构相关的高计算成本，这种结构需要大量的函数来进行优化过程和可靠性分析。已经开发了几种去耦方法来提高RBDO的效率。此外，替代模型已被用来替代原始的耗时模型并提高了计算效率。本文提出了一种新的基于分位数的顺序RBDO方法，该方法使用Kriging替代模型解决具有独立约束函数的问题。集成了一个错误控制的自适应Kriging方案，以得出替代模型的准确性信息，并制定了一种有助于对性能函数模型进行独立训练的策略。所提出的独立训练避免了不必要的性能函数评估，同时确保了可靠性估计的准确性。此外，提出了一种新的采样方法，该方法允许针对确定性和概率性约束都完善代理模型。进行了五个数值算例，证明了该方法的性能。可以看到，与给定约束函数是独立的基于代理模型的最新方法相比，所提出的方法能够以更少的函数评估收敛到最佳设计。提出了一种新的采样方法，该方法允许针对确定性和概率约束都完善代理模型。进行了五个数值算例，证明了该方法的性能。可以看到，与给定约束函数是独立的基于代理模型的最新方法相比，所提出的方法能够以更少的函数评估收敛到最佳设计。提出了一种新的采样方法，该方法允许针对确定性和概率约束都完善代理模型。进行了五个数值算例，证明了该方法的性能。可以看到，与给定约束函数是独立的基于代理模型的最新方法相比，所提出的方法能够以更少的函数评估收敛到最佳设计。